Obtaining Better Static Word Embeddings Using Contextual Embedding Models (2021.acl-long)
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| Challenge: | Recent contextual word embeddings have prohibitively high computational cost in many use-cases and are hard to interpret. |
| Approach: | They propose a distillation method which is an extension of CBOW-based training and improves computational efficiency of NLP applications. |
| Outcome: | The proposed method outperforms existing models and existing models in terms of quality and performance. |
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| Challenge: | Contextualized word representations are effective in downstream tasks such as question answering, named entity recognition, and sentiment analysis. |
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Embeddings in Natural Language Processing (2020.coling-tutorials)
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| Challenge: | Embeddings have been a key topic of interest in NLP for the past decade . a quick warm-up introduction to NLP and why it is important to have a semantic comprehension of texts . |
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Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings (2020.acl-main)
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| Challenge: | Contextualized representations have become the default for downstream NLP applications. |
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Better Word Embeddings by Disentangling Contextual n-Gram Information (N19-1)
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Towards Better Context-aware Lexical Semantics:Adjusting Contextualized Representations through Static Anchors (2020.emnlp-main)
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| Challenge: | Recent research has shown that contextualized models generate dynamic embeddings for words in context, but static embedds are often overlooked in this trend towards contextualized modeling. |
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| Challenge: | contextual language models are dominant in the field of Natural Language Processing, but they are not suitable for all uses. |
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Contextual Embeddings: When Are They Worth It? (2020.acl-main)
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| Challenge: | In recent years, rich contextual embeddings have enabled rapid progress on benchmarks like GLUE, but require significant computational resources during pretraining and during downstream task training and inference. |
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Language Modelling Makes Sense: Propagating Representations through WordNet for Full-Coverage Word Sense Disambiguation (P19-1)
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| Challenge: | Contextual embeddings address the problem of meaning conflation hampering word embeddables. |
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Exploring Category Structure with Contextual Language Models and Lexical Semantic Networks (2023.eacl-main)
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| Challenge: | Recent work on word embeddings reports low correlations with human ratings . contextual language models (CLMs) have been successful in acquiring semantic and world knowledge. |
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More Embeddings, Better Sequence Labelers? (2020.findings-emnlp)
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| Challenge: | Existing work suggests contextual embeddings improve sequence labeling accuracy . but, there is no definite conclusion on whether concatenating different kinds of embeddables is effective . |
| Approach: | They propose a family of contextual embeddings that improves sequence labeling accuracy . they conduct extensive experiments on 3 tasks over 18 datasets and 8 languages . |
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